Discriminatively Trained Action Recognition Model Based on Hierarchical Part Tree
QIAN Yinzhong1,2,3, SHEN Yifan1,2
1.Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433 2.School of Computer Science, Fudan University, Shanghai 200433 3.School of Software, Changzhou College of Information Technology, Changzhou 213164
Abstract：Action recognition of body pose from static image is exploited in this paper. A hierarchical part tree structure is proposed. In the structure, each node is composed by a collection of poselets to represent its pose variation and pairs of linked nodes are constrained to form a pictorial structure. Grounded on the structure, a discriminatively trained action recognition model based on hierarchical part tree is presented. Except for deforming cost, the pairwise potential function in the model introduces co-occurrence cost. Parent part contains child part and the relative position of linked nodes is described by normal distribution, and thus the matching procedure is inferred efficiently in the framework of distance transform and message passing. Three models with different number of nodes by trimming the tree are comparatively evaluated on two datasets. Experimental results demonstrate that coarse parts in former two layers have strong saliency for action recognition, the recognition capability is further improved by body parts in the third layer, and the anatomical stick parts in the fourth layer are basically not useful for action recognition.
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2017, Vol. 30 
